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Dynamic contrast-based quantization for lossy wavelet image compression.

Damon M Chandler1, Sheila S Hemami

  • 1School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA. dmc27@cornell.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 14, 2005
PubMed
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This study introduces a novel contrast-based quantization strategy for wavelet image compression. It enhances visual quality at all bit rates by minimizing perceived distortions, outperforming existing methods.

Area of Science:

  • Image processing
  • Computer vision
  • Visual perception

Background:

  • Wavelet image compression is crucial for efficient data storage and transmission.
  • Existing methods often struggle to balance compression ratios with visual fidelity.
  • Psychophysical studies provide insights into human visual system's perception of distortions.

Purpose of the Study:

  • To develop a contrast-based quantization strategy for lossy wavelet image compression.
  • To preserve visual quality across a range of bit rates.
  • To integrate psychophysical findings into image compression algorithms.

Main Methods:

  • Utilizing psychophysical experimental results on wavelet subband quantization distortions.
  • Quantizing subbands based on root-mean-squared contrasts, considering image, subband, and display characteristics.

Related Experiment Videos

  • Implementing a unified framework for both non-embedded and embedded quantization.

    • Main Results:

    • The proposed strategy yields competitive visual quality at high bit rates compared to visually lossless approaches.
    • It demonstrates improved visual quality over current visually lossy approaches at low bit rates.
    • The embedded quantization variant produces a scalable codestream maintaining visual quality at all bit rates.

    Conclusions:

    • The contrast-based quantization strategy effectively preserves visual quality in lossy wavelet image compression.
    • This approach offers a unified framework applicable to various quantization contexts, including JPEG-2000.
    • It represents a significant advancement in achieving high visual fidelity at diverse compression levels.